Clinical model for Hereditary Transthyretin Amyloidosis age of onset prediction - Pedroto, Maria; Coelho, Teresa; Jorge, Alípio; Mendes-Moreira, João (Frontiers in Neurology, 2023)
Introduction: Hereditary transthyretin amyloidosis (ATTRv amyloidosis) is a rare neurological hereditary disease clinically characterized as severe, progressive, and life-threatening while the age of onset represents the moment in time when the first symptoms are felt. In this paper, we present and discuss our results on the study, development, and evaluation of an approach that allows for time-to-event prediction of the age of onset, while focusing on genealogical feature construction.This research was triggered by the need to answer the medical problem of when will an asymptomatic ATTRv patient show symptoms of the disease. To do so, we defined and studied the impact of 77 features (ranging from demographic, genealogical, to familial disease history); studied and compared a pool of prediction algorithms, namely Linear Regression (LR), Elastic Net (EN), Lasso (LA), Ridge (RI), Support Vector Machines (SV), Decision Tree (DT), Random Forest (RF), and XGboost (XG), both in a classification as well as a regression setting; assembled a Baseline (BL) which corresponds to the current medical knowledge of the disease; studied the problem of predicting the age of onset of ATTRv patients; assessed the viability of predicting age of onset on short term horizons, with a classification framing, on localized sets of patients (currently symptomatic and asymptomatic carriers, with and without genealogical information); and compared the results with an out-of-bag evaluation set, assembled in a different time-frame than the original data, in order to account for data leakage.Results: Currently, we observe that our approach outperforms the BL model, which follows a set of clinical heuristics and represents current medical practice. Overall, our results show the 1 Pedroto et al.supremacy of SV and XG for both the prediction tasks, although impacted by data characteristics, namely the existence of missing values, complex data, and small-sized available inputs. Discussion: With this work we defined a predictive model approach capable to be well understood by medical professionals, compared with the current practice, namely the baseline approach (BL), and successfully showed the improvement achieved to the current medical knowledge.